The CALIS Procedure
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Overview
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Getting Started
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Syntax
Classes of Statements in PROC CALIS Single-Group Analysis Syntax Multiple-Group Multiple-Model Analysis Syntax PROC CALIS Statement BOUNDS Statement BY Statement COSAN Statement COV Statement DETERM Statement EFFPART Statement FACTOR Statement FITINDEX Statement FREQ Statement GROUP Statement LINCON Statement LINEQS Statement LISMOD Statement LMTESTS Statement MATRIX Statement MEAN Statement MODEL Statement MSTRUCT Statement NLINCON Statement NLOPTIONS Statement OUTFILES Statement PARAMETERS Statement PARTIAL Statement PATH Statement PCOV Statement PVAR Statement RAM Statement REFMODEL Statement RENAMEPARM Statement SAS Programming Statements SIMTESTS Statement STD Statement STRUCTEQ Statement TESTFUNC Statement VAR Statement VARIANCE Statement VARNAMES Statement WEIGHT Statement
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Details
Input Data Sets Output Data Sets The COSAN Model The FACTOR Model The LINEQS Model The LISMOD Model and Submodels The MSTRUCT Model The PATH Model The RAM Model Naming Variables and Parameters Setting Constraints on Parameters Automatic Variable Selection Estimation Criteria Relationships among Estimation Criteria Gradient, Hessian, Information Matrix, and Approximate Standard Errors Counting the Degrees of Freedom Assessment of Fit Total, Direct, and Indirect Effects Standardized Solutions Modification Indices Missing Values and the Analysis of Missing Patterns Measures of Multivariate Kurtosis Initial Estimates Use of Optimization Techniques Computational Problems Displayed Output ODS Table Names ODS Graphics
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Examples
Estimating Covariances and Correlations Estimating Covariances and Means Simultaneously Testing Uncorrelatedness of Variables Testing Covariance Patterns Testing Some Standard Covariance Pattern Hypotheses Linear Regression Model Multivariate Regression Models Measurement Error Models Testing Specific Measurement Error Models Measurement Error Models with Multiple Predictors Measurement Error Models Specified As Linear Equations Confirmatory Factor Models Confirmatory Factor Models: Some Variations The Full Information Maximum Likelihood Method Comparing the ML and FIML Estimation Path Analysis: Stability of Alienation Simultaneous Equations with Mean Structures and Reciprocal Paths Fitting Direct Covariance Structures Confirmatory Factor Analysis: Cognitive Abilities Testing Equality of Two Covariance Matrices Using a Multiple-Group Analysis Testing Equality of Covariance and Mean Matrices between Independent Groups Illustrating Various General Modeling Languages Testing Competing Path Models for the Career Aspiration Data Fitting a Latent Growth Curve Model Higher-Order and Hierarchical Factor Models Linear Relations among Factor Loadings Multiple-Group Model for Purchasing Behavior Fitting the RAM and EQS Models by the COSAN Modeling Language Second-Order Confirmatory Factor Analysis Linear Relations among Factor Loadings: COSAN Model Specification Ordinal Relations among Factor Loadings Longitudinal Factor Analysis
- References
| Missing Values and the Analysis of Missing Patterns |
If the DATA= data set contains raw data (rather than a covariance or correlation matrix), in general observations with missing values for any variables in the analysis are omitted from the computations. The only exception is with METHOD=FIML. Incomplete observations with at least one nonmissing variables in the analysis are also used for the estimation.
If a covariance or correlation matrix is read, missing values are allowed as long as every pair of variables has at least one nonmissing value. Unlike the raw data input, METHOD=FIML does not allow missing values in the covariance or correlation matrix.
When you use METHOD=FIML, PROC CALIS provide several analyses on the missing patterns of the raw input data sets. First, PROC CALIS shows the coverage results for the means and covariances. The coverage results refer to the proportions of data present for computing the means and the covariances. Because distinct missing patterns in the data sets are possible, the coverage proportions for the individual means and covariances could vary. Average coverage proportions of the means and covariances give you an overall idea about the missingness (or the lack of). In order to help locate the problematic means and covariances that have the low coverage, PROC CALIS shows the rank orders of the smallest coverages of mean and covariance elements. The number of smallest coverages shown for the means is equal to half of the total number of variables. The number of smallest coverages shown for the covariances is equal to half of the total number of the distinct elements in the lower triangular of the covariance matrix. However, in both cases at most 10 smallest coverages would be shown.
Second, PROC CALIS ranks the most frequent missing patterns in the data set (the nonmissing pattern is excluded in the ranking). Because the number of missing patterns could be quite large, PROC CALIS displays only a limited number of most frequent missing patterns in the output. You can use the MAXMISSPAT= and the TMISSPAT= options to control the number of missing patterns to display. See these options for details.
Third, PROC CALIS shows the means of the most frequent missing patterns, along with the means for the nonmissing pattern for comparison.
See Example 26.14 for an illustration of the use of the full information maximum likelihood method and the analysis of missing patterns.